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Mid-level metro MLOps role with in-demand skills creates moderate competition.
MLOps, cloud, and Spark skills are transferable across industries, moderate domain specificity.
Explicit 5-8 years and multiple mandatory MLOps, cloud, and infra skills.
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Design, develop, deploy, and maintain scalable ML pipelines including data ingestion, feature engineering, model training, validation, deployment, and monitoring.
Implement MLOps best practices such as experiment tracking, model versioning, CI/CD, model governance, automated retraining, and build endpoints/inference services for real-time and batch scoring.
Develop and deploy GenAI applications leveraging LLMs, RAG frameworks, vector databases, and prompt engineering, while collaborating with DevOps to optimize cloud infrastructure and scalability.
5+ years of experience in Machine Learning Engineering, Data Science, MLOps, or Data Engineering.
Proficiency with MLOps platforms (MLflow, Azure ML, Databricks), CI/CD pipelines, Git/GitHub, Docker, Kubernetes.
Experience with Spark/PySpark, distributed data processing, and deploying production ML models.
Exposure to cloud platforms (Azure, AWS, or GCP), Kafka or streaming frameworks, Python programming, and Generative AI use case deployment with prompt engineering and RAG Framework.
Experienced in end-to-end MLOps implementation with deep knowledge of model governance, monitoring, and automated retraining workflows.
Strong background in both Data Engineering and ML deployment supporting GenAI and LLM-based applications in cloud environments.
Comfortable working cross-functionally with Data Scientists, Data Engineers, and DevOps teams to operationalize and scale AI solutions reliably in production.